descriptive_statistics

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of behavioral disclosure. It reveals that the tool computes summaries, tracks missingness, and supplies robust boundaries, implying a read-only computation. It does not explicitly state there are no side effects or clarify what 'sample flow' means, but the stated output topics provide some behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with no filler, and the core purpose is front-loaded. The phrasing is efficient, though the terms 'sample flow' and 'robust boundaries' are terse and could be clearer for the target audience.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the output schema exists, return values need no explanation. The description plus schema gives enough to call the tool with numeric data, but it lacks clarification on ambiguous terms and offers no usage context or example. It is minimally viable but leaves meaningful gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for the schema's silence, but it only says 'numeric variables,' which weakly maps to the required 'data' parameter. It does not explain the nested 'values' array shape or the optional 'variable_names' field, leaving the agent to infer parameter semantics from the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Summarize') and resource ('numeric variables'), and names the output topics: sample flow, missingness, and robust boundaries. This makes the tool's role clear and distinguishes it from siblings like correlation_matrix or ctt_item_analysis, though the domain jargon 'sample flow' is somewhat ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for descriptively summarizing numeric data, but it gives no explicit when-to-use guidance and does not reference alternatives among the sibling tools. There is clear context, but no exclusions or direct comparison to other tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.